Contrast-dependent nonlinearities arise locally in a model of contrast-invariant orientation tuning

Response sub-additivity and variability quenching in visual cortex

Sub-additivity and variability are ubiquitous response motifs in the primary visual cortex (V1). Response sub-additivity enables the construction of useful interpretations of the visual environment, whereas response variability indicates the factors that limit the precision with which the brain can do this. There is increasing evidence that experimental manipulations that elicit response sub-additivity often also quench response variability. Here, we provide an overview of these phenomena and suggest that they may have common origins. We discuss empirical findings and recent model-based insights into the functional operations, computational objectives and circuit mechanisms underlying V1 activity. These different modelling approaches all predict that response sub-additivity and variability quenching often co-occur. The phenomenology of these two response motifs, as well as many of the insights obtained about them in V1, generalize to other cortical areas. Thus, the connection between response sub-additivity and variability quenching may be a canonical motif across the cortex.

A novel mutual information estimator to measure spike train correlations in a model thalamocortical network

The impact of thalamic state on information transmission to the cortex remains poorly understood. This limitation exists due to the rich dynamics displayed by thalamocortical networks and because of inadequate tools to characterize those dynamics. Here, we introduce a novel estimator of mutual information and use it to determine the impact of a computational model of thalamic state on information transmission. Using several criteria, this novel estimator, which uses an adaptive partition, is shown to be superior to other mutual information estimators with uniform partitions when used to analyze simulated spike train data with different mean spike rates, as well as electrophysiological data from simultaneously recorded neurons. When applied to a thalamocortical model, the estimator revealed that thalamocortical cell T-type calcium current conductance influences mutual information between the input and output from this network. In particular, a T-type calcium current conductance of ~40 nS appears to produce maximal mutual information between the input to this network (conceptualized as afferent input to the thalamocortical cell) and the output of the network at the level of a layer 4 cortical neuron. Furthermore, at particular combinations of inputs to thalamocortical and thalamic reticular nucleus cells, thalamic cell bursting correlated strongly with recovery of mutual information between thalamic afferents and layer 4 neurons. These studies suggest that the novel mutual information estimator has advantages over previous estimators and that thalamic reticular nucleus activity can enhance mutual information between thalamic afferents and thalamorecipient cells in the cortex. NEW & NOTEWORTHY In this study, a novel mutual information estimator was developed to analyze information flow in a model thalamocortical network. Our findings suggest that this estimator is a suitable tool for signal transmission analysis, particularly in neural circuits with disparate firing rates, and that the thalamic reticular nucleus can potentiate ascending sensory signals, while thalamic recipient cells in the cortex can recover mutual information in ascending sensory signals that is lost due to thalamic bursting.

Comparison of mechanisms for contrast-invariance of orientation selectivity in simple cells

The simple cells of the visual cortex respond over a narrow range of stimulus orientations, and this tuning is invariant to the contrast at which the stimulus is presented. The inputs to a single cell derive from a population of thalamic cells that provide a bell-shaped tuning width and offset that increases with stimulus contrast. Synaptic depression, noise and inhibition have been proposed as feedforward mechanisms to explain why these increases do not appear in simple cells. The extent to which these three mechanisms contribute to contrast-invariant orientation tuning is unknown. Consequently, the aim was to test the hypothesis that these mechanisms do not contribute equally. Unlike previous studies, all mechanisms were examined using the same network model based on Banitt et al. (2007). The results showed that thalamocortical synaptic noise was essential and sufficient to widen tuning widths at low contrasts to that of higher contrasts but could not counteract the offset at higher contrasts. Thalamocortical synaptic depression could only be used to counteract a small fraction of the offset otherwise the relationship between contrast and response rate was disrupted. Only broadly tuned simple and complex cell inhibition could counteract the remaining offset for all stimulus contrasts but complex cell inhibition reduced the gain of the response. These results suggest unequal contributions of these feedforward mechanisms with thalamic synaptic noise widening tuning widths for low contrasts, synaptic depression counteracting a small component of the offset and synaptic inhibition completely removing the remaining offset to produce contrast-invariant orientation tuning.

Stimulus Contrast and Retinogeniculate Signal Processing

Neuronal signals conveying luminance contrast play a key role in nearly all aspects of perception, including depth perception, texture discrimination, and motion perception. Although much is known about the retinal mechanisms responsible for encoding contrast information, relatively little is known about the relationship between stimulus contrast and the processing of neuronal signals between visual structures. Here, we describe simultaneous recordings from monosynaptically connected retinal ganglion cells and lateral geniculate nucleus (LGN) neurons in the cat to determine how stimulus contrast affects the communication of visual signals between the two structures. Our results indicate that: (1) LGN neurons typically reach their half-maximal response at lower contrasts than their individual retinal inputs and (2) LGN neurons exhibit greater contrast-dependent phase advance (CDPA) than their retinal inputs. Further analyses suggests that increased sensitivity relies on spatial convergence of multiple retinal inputs, while increased CDPA is achieved, in part, on temporal summation of arriving signals.

Canonical computations of cerebral cortex

The idea that there is a fundamental cortical circuit that performs canonical computations remains compelling though far from proven. Here we review evidence for two canonical operations within sensory cortical areas: a feedforward computation of selectivity; and a recurrent computation of gain in which, given sufficiently strong external input, perhaps from multiple sources, intracortical input largely, but not completely, cancels this external input. This operation leads to many characteristic cortical nonlinearities in integrating multiple stimuli. The cortical computation must combine such local processing with hierarchical processing across areas. We point to important changes in moving from sensory cortex to motor and frontal cortex and the possibility of substantial differences between cortex in rodents vs. species with columnar organization of selectivity.

The effects of short-term synaptic depression at thalamocortical synapses on orientation tuning in cat V1

We examine the effects of short-term synaptic depression on the orientation tuning of the LGN input to simple cells in cat primary visual cortex (V1). The total LGN input has an untuned component as well as a tuned component, both of which grow with stimulus contrast. The untuned component is not visible in the firing rate responses of the simple cells. The suppression of the contribution of the untuned input component to firing rate responses is key to establishing orientation selectivity and its invariance with stimulus contrast. It has been argued that synaptic depression of LGN inputs could contribute to the selective suppression of the untuned component and thus contribute to the tuning observed in simple cells. We examine this using a model fit to the depression observed at thalamocortical synapses in-vivo, and compare this to an earlier model fit based on in-vitro observations. We examine the tuning of both the conductance and the firing rate induced in simple cells by the net LGN input. We find that depression causes minimal suppression of the untuned component. The primary effect of depression is to cause the contrast response curve to saturate at lower contrasts without differentially affecting the tuned vs. untuned components. This effect is slightly weaker for in-vivo vs. in-vitro parameters. Thus, synaptic depression of LGN inputs does not appreciably contribute to the orientation tuning of V1 simple cells.

Corticocortical feedback increases the spatial extent of normalization

Normalization has been proposed as a canonical computation operating across different brain regions, sensory modalities, and species. It provides a good phenomenological description of non-linear response properties in primary visual cortex (V1), including the contrast response function and surround suppression. Despite its widespread application throughout the visual system, the underlying neural mechanisms remain largely unknown. We recently observed that corticocortical feedback contributes to surround suppression in V1, raising the possibility that feedback acts through normalization. To test this idea, we characterized area summation and contrast response properties in V1 with and without feedback from V2 and V3 in alert macaques and applied a standard normalization model to the data. Area summation properties were well explained by a form of divisive normalization, which computes the ratio between a neuron's driving input and the spatially integrated activity of a "normalization pool." Feedback inactivation reduced surround suppression by shrinking the spatial extent of the normalization pool. This effect was independent of the gain modulation thought to mediate the influence of contrast on area summation, which remained intact during feedback inactivation. Contrast sensitivity within the receptive field center was also unaffected by feedback inactivation, providing further evidence that feedback participates in normalization independent of the circuit mechanisms involved in modulating contrast gain and saturation. These results suggest that corticocortical feedback contributes to surround suppression by increasing the visuotopic extent of normalization and, via this mechanism, feedback can play a critical role in contextual information processing.

Modeling lateral geniculate nucleus response with contrast gain control. Part 2: analysis

Cope et al. [J. Opt. Soc. Am. A30, 2401 (2013)] proposed a class of models for lateral geniculate nucleus (LGN) ON-cell behavior consisting of a linear response with divisive normalization by local stimulus contrast. Here, we analyze a specific model with the linear response defined by a difference-of-Gaussians filter, and a circular Gaussian for the gain pool weighting function. For sinusoidal grating stimuli, the parameter region for bandpass behavior of the linear response is determined, and the gain control response is shown to act as a switch (changing from "off" to "on" with increasing spatial frequency). It is also shown that large gain pools stabilize the optimal spatial frequency of the total nonlinear response at a fixed value independent of contrast and stimulus magnitude. Under- and super-saturation, as well as contrast saturation, occur as typical effects of stimulus magnitude. For circular spot stimuli, it is shown that large gain pools stabilize the spot size that yields the maximum response.

Modeling lateral geniculate nucleus response with contrast gain control. Part 1: formulation

A class of models for lateral geniculate nucleus (LGN) on-cell behavior is proposed. The models consist of a linear filter with divisive normalization by root mean square local contrast and include an intrinsic noise density parameter. The properties of these models are shown to match observed LGN behavior: (1) a linear response to low-magnitude stimuli; (2) a linear response without saturation (luxotonic behavior) for zero-contrast stimuli (homogeneous fields) with increasing magnitude; and (3) response saturation for nonzero contrast stimuli with increasing magnitude. The models possess an intrinsic scale for signal-to-noise ratio (SNR). The models show under and supersaturation, as well as saturation, for sinusoidal grating stimuli with increasing contrast and predict that different SNR regimes will cause a single neuron to show different contrast response curves. A companion paper [1] provides a detailed analysis of the full nonlinear response for sinusoidal grating stimuli and circular spot stimuli.

Feedforward origins of response variability underlying contrast invariant orientation tuning in cat visual cortex

Contrast invariant orientation tuning in simple cells of the visual cortex depends critically on contrast dependent trial-to-trial variability in their membrane potential responses. This observation raises the question of whether this variability originates from within the cortical circuit or the feedforward inputs from the lateral geniculate nucleus (LGN). To distinguish between these two sources of variability, we first measured membrane potential responses while inactivating the surrounding cortex, and found that response variability was nearly unaffected. We then studied variability in the LGN, including contrast dependence, and the trial-to-trial correlation in responses between nearby neurons. Variability decreased significantly with contrast, whereas correlation changed little. When these experimentally measured parameters of variability were applied to a feedforward model of simple cells that included realistic mechanisms of synaptic integration, contrast-dependent, orientation independent variability emerged in the membrane potential responses. Analogous mechanisms might contribute to the stimulus dependence and propagation of variability throughout the neocortex.

Power-law input-output transfer functions explain the contrast-response and tuning properties of neurons in visual cortex

We develop a unified model accounting simultaneously for the contrast invariance of the width of the orientation tuning curves (OT) and for the sigmoidal shape of the contrast response function (CRF) of neurons in the primary visual cortex (V1). We determine analytically the conditions for the structure of the afferent LGN and recurrent V1 inputs that lead to these properties for a hypercolumn composed of rate based neurons with a power-law transfer function. We investigate what are the relative contributions of single neuron and network properties in shaping the OT and the CRF. We test these results with numerical simulations of a network of conductance-based model (CBM) neurons and we demonstrate that they are valid and more robust here than in the rate model. The results indicate that because of the acceleration in the transfer function, described here by a power-law, the orientation tuning curves of V1 neurons are more tuned, and their CRF is steeper than those of their inputs. Last, we show that it is possible to account for the diversity in the measured CRFs by introducing heterogeneities either in single neuron properties or in the input to the neurons. We show how correlations among the parameters that characterize the CRF depend on these sources of heterogeneities. Comparison with experimental data suggests that both sources contribute nearly equally to the diversity of CRF shapes observed in V1 neurons.

Both the response and membrane potential of neurons in the primary visual cortex (V1) are selective to the orientation of elongated stimuli. The widths of the tuning curves, which characterize this selectivity, hardly depend on stimulus contrast whereas their amplitude does. The contrast dependence of this amplitude, the contrast response function (CRF), has a sigmoidal shape. Saturation of the spike response is substantially lower than the neurons' maximal firing rate. These well established facts constrain the possible mechanisms for orientation selectivity in V1. Furthermore, the single neuron CRFs in V1 display a broad diversity in their shape. This adds other constraints. Many theoretical works have tried to elaborate mechanisms of orientation selectivity that are compatible with the contrast invariant tuning widths. However, these mechanisms are usually incompatible with sigmoidal CRFs. We propose a mechanism which accounts simultaneously for contrast invariant tuning width for both rate and voltage response and for the shape and diversity of the CRFs. This mechanism relies on the interplay between power-law frequency-current transfer functions of single neurons, as measured in vivo in cortex, and on the recurrent interactions in the cortical circuit.

Contrast gain control and cortical TrkB signaling shape visual acuity

During development and aging and in amblyopia, visual acuity is far below the limitations set by the retina. Expression of brain-derived neurotrophic factor (BDNF) in the visual cortex is reduced in these situations. We asked whether neurotrophic tyrosine kinase receptor, type 2 (TrkB) regulates cortical visual acuity in adult mice. We found that genetically interfering with TrkB/BDNF signaling in pyramidal cells in the mature visual cortex reduced synaptic strength and resulted in a loss of neural responses to high spatial-frequency stimuli. Responses to low spatial-frequency stimuli were unaffected. This selective loss was not accompanied by a change in receptive field sizes or plasticity, but apparent contrast was reduced. Our results indicate that a dependence on spatial frequency in the Heeger normalization model explains this selective effect of contrast reduction on high-resolution vision and suggest that it involves contrast gain control operating in the visual cortex.

Complex dynamics of V1 population responses explained by a simple gain-control model

To understand sensory encoding and decoding, it is essential to characterize the dynamics of population responses in sensory cortical areas. Using voltage-sensitive dye imaging in awake, fixating monkeys, we obtained complete quantitative measurements of the spatiotemporal dynamics of V1 responses over the entire region activated by small, briefly presented stimuli. The responses exhibit several complex properties: they begin to rise approximately simultaneously over the entire active region, but reach their peak more rapidly at the center. However, at stimulus offset the responses fall simultaneously and at the same rate at all locations. Although response onset depends on stimulus contrast, both the peak spatial profile and the offset dynamics are independent of contrast. We show that these results are consistent with a simple population gain-control model that generalizes earlier single-neuron contrast gain-control models. This model provides valuable insight and is likely to be applicable to other brain areas.

Adaptive integration in the visual cortex by depressing recurrent cortical circuits

Neurons in the visual cortex receive a large amount of input from recurrent connections, yet the functional role of these connections remains unclear. Here we explore networks with strong recurrence in a computational model and show that short-term depression of the synapses in the recurrent loops implements an adaptive filter. This allows the visual system to respond reliably to deteriorated stimuli yet quickly to high-quality stimuli. For low-contrast stimuli, the model predicts long response latencies, whereas latencies are short for high-contrast stimuli. This is consistent with physiological data showing that in higher visual areas, latencies can increase more than 100 ms at low contrast compared to high contrast. Moreover, when presented with briefly flashed stimuli, the model predicts stereotypical responses that outlast the stimulus, again consistent with physiological findings. The adaptive properties of the model suggest that the abundant recurrent connections found in visual cortex serve to adapt the network's time constant in accordance with the stimulus and normalizes neuronal signals such that processing is as fast as possible while maintaining reliability.

The functional anatomy of a perceptual decision in the human brain

Our ability to make rapid decisions based on sensory information belies the complexity of the underlying computations. Recently, "accumulator" models of decision making have been shown to explain the activity of parietal neurons as macaques make judgments concerning visual motion. Unraveling the operation of a decision-making circuit, however, involves understanding both the responses of individual components in the neural circuitry and the relationships between them. In this functional magnetic resonance imaging study of the decision process in humans, we demonstrate that an accumulator model predicts responses to visual motion in the intraparietal sulcus (IPS). Significantly, the metrics used to define responses within the IPS also reveal distinct but interacting nodes in a circuit, including early sensory detectors in visual cortex, the visuomotor integration system of the IPS, and centers of cognitive control in the prefrontal cortex, all of which collectively define a perceptual decision-making network.

Adaptive gain modulation in V1 explains contextual modifications during bisection learning

The neuronal processing of visual stimuli in primary visual cortex (V1) can be modified by perceptual training. Training in bisection discrimination, for instance, changes the contextual interactions in V1 elicited by parallel lines. Before training, two parallel lines inhibit their individual V1-responses. After bisection training, inhibition turns into non-symmetric excitation while performing the bisection task. Yet, the receptive field of the V1 neurons evaluated by a single line does not change during task performance. We present a model of recurrent processing in V1 where the neuronal gain can be modulated by a global attentional signal. Perceptual learning mainly consists in strengthening this attentional signal, leading to a more effective gain modulation. The model reproduces both the psychophysical results on bisection learning and the modified contextual interactions observed in V1 during task performance. It makes several predictions, for instance that imagery training should improve the performance, or that a slight stimulus wiggling can strongly affect the representation in V1 while performing the task. We conclude that strengthening a top-down induced gain increase can explain perceptual learning, and that this top-down signal can modify lateral interactions within V1, without significantly changing the classical receptive field of V1 neurons.

Contrast induced changes in response latency depend on stimulus specificity

Neurones in visual cortex show increasing response latency with decreasing stimulus contrast. Neurophysiological recordings from neurones in inferior temporal cortex (IT) and the superior temporal sulcus (STS), show that the increment in response latency with decreasing stimulus contrast is considerably greater in higher visual areas than that seen in primary visual cortex. This suggests that the majority of the latency change is not retinal or V1 in origin, instead each cortical processing area adds latency at low contrast. I show that, as in earlier visual areas, response latency is more strongly dependent on stimulus contrast than stimulus identity. There is large variation in the extent to which response latency increases with decreasing stimulus contrast. I show that this between cell variability is, at least in part, related to the stimulus specificity of the neurones: the increase in response latency as stimulus contrast decreases is greater for neurones that respond to few stimuli compared to neurones that respond to many stimuli.

Functional geometry of the horizontal connectivity in the primary visual cortex

We present a geometrical model of the functional architecture of the primary visual cortex. In particular we describe the geometric structure of connections found both in neurophysiological and psychophysical experiments, modeling both co-axial and trans-axial excitatory connections. The model shows what could be the deep structure for both boundary and figure completion and for morphological structures such as the medial axis of a shape.

A biologically realistic model of contrast invariant orientation tuning by thalamocortical synaptic depression

Simple cells in layer 4 of the primary visual cortex of the cat show contrast-invariant orientation tuning, in which the amplitude of the peak response is proportional to the stimulus contrast but the width of the tuning curve hardly changes with contrast. This study uses a detailed model of spiny stellate cells (SSCs) from cat area 17 to explain this property. The model integrates our experimental data, including morphological and intrinsic membrane properties and the number and spatial distribution of four major synaptic input sources of the SSC: the dorsal lateral geniculate nucleus (dLGN) and three cortical sources. The model also includes synaptic properties of these inputs. The cortical input served as sources of background activity, and visual stimuli was modeled as sinusoidal grating. For all contrasts, strong synaptic depression of the dLGN feedforward afferents compresses the firing rates in response to orthogonal stimuli, keeping these rates at practically the same low level. However, at preferred orientations, despite synaptic depression, firing rate changes as a function of contrast. Thus, when embedded in an active network, strong synaptic depression can explain contrast-invariant orientation tuning of simple cells. This is true also when the dLGN inputs are partially depressed as a result of their spontaneous activity and to some extent also when parameters were fitted to a more moderate level of synaptic depression. The model response is in close agreement with experimental results, in terms of both output spikes and membrane voltage (amplitude and fluctuations), with reasonable exceptions given that recurrent connections were not incorporated.

Asymmetric synaptic depression in cortical networks

Synaptic depression is essential for controlling the balance between excitation and inhibition in cortical networks. Several studies have shown that the depression of intracortical synapses is asymmetric, that is, inhibitory synapses depress less than excitatory ones. Whether this asymmetry has any impact on cortical function is unknown. Here we show that the differential depression of intracortical synapses provides a mechanism through which the gain and sensitivity of cortical circuits shifts over time to improve stimulus coding. We examined the functional consequences of asymmetric synaptic depression by modeling recurrent interactions between orientation-selective neurons in primary visual cortex (V1) that adapt to feedforward inputs. We demonstrate analytically that despite the fact that excitatory synapses depress more than inhibitory synapses, excitatory responses are reduced less than inhibitory ones to increase the overall response gain. These changes play an active role in generating selective gain control in visual cortical circuits. Specifically, asymmetric synaptic depression regulates network selectivity by amplifying responses and sensitivity of V1 neurons to infrequent stimuli and attenuating responses and sensitivity to frequent stimuli, as is indeed observed experimentally.

The emergence of contrast-invariant orientation tuning in simple cells of cat visual cortex

Simple cells in primary visual cortex exhibit contrast-invariant orientation tuning, in seeming contradiction to feed-forward models that rely on lateral geniculate nucleus (LGN) input alone. Contrast invariance has therefore been thought to depend on the presence of intracortical lateral inhibition. In vivo intracellular recordings instead suggest that contrast invariance can be explained by three properties of the excitatory pathway. (1) Depolarizations evoked by orthogonal stimuli are determined by the amount of excitation a cell receives from the LGN, relative to the excitation it receives from other cortical cells. (2) Depolarizations evoked by preferred stimuli saturate at lower contrasts than the spike output of LGN relay cells. (3) Visual stimuli evoke contrast-dependent changes in trial-to-trial variability, which lead to contrast-dependent changes in the relationship between membrane potential and spike rate. Thus, high-contrast, orthogonally oriented stimuli that evoke significant depolarizations evoke few spikes. Together these mechanisms, without lateral inhibition, can account for contrast-invariant stimulus selectivity.

Comparison Among Some Models of Orientation Selectivity

Several models exist for explaining primary visual cortex (V1) orientation tuning. The modified feedforward model (MFM) and the recurrent model (RM) are major examples. We have implemented these two models, at the same level of detail, alongside a few newer variations, and thoroughly compared their receptive-field structures. We found that antiphase inhibition in the MFM enhances both spatial phase information and orientation tuning, producing well-tuned simple cells. This remains true for a newer version of the MFM that incorporates untuned complex-cell inhibition. In contrast, when the recurrent connections in the RM are strong enough to produce typical V1 orientation tuning, they also eliminate spatial phase information, making the cells complex. Introducing phase specificity into the connections of the RM (as done in an original version of the RM) can make the cells phase sensitive, but the cells show an incorrect 90° peak shift of orientation tuning under opposite contrast signs. An inhibition-dominant version of the RM can generate well-tuned cells across the simple–complex spectrum, but it predicts that the net effect of cortical interactions is to suppress feedforward excitation across all orientations in simple cells. Finally, adding antiphase inhibition used in the MFM into the RM produces a most general model. We call this new model the modified recurrent model (MRM) and show that this model can also produce well-tuned cells throughout the simple–complex spectrum. Unlike the inhibition-dominant RM, the MRM is consistent with data from cat V1, suggesting that the net effect of cortical interactions is to boost simple cell responses at the preferred orientation. These results suggest that the MFM is well suited for explaining orientation tuning in simple cells, whereas the standard RM is for complex cells. The assignment of the RM to complex cells also avoids conflicts between the RM and the experiments of cortical inactivation (done on simple cells) and the spatial-frequency dependency of orientation tuning (found in simple cells). Because orientation-tuned V1 cells show a continuum of simple- to complex-cell behavior, the MRM provides the best description of V1 data.

Chromatic gain controls in visual cortical neurons

Although the response of a neuron in the visual cortex generally grows nonlinearly with contrast, the spatial tuning of the cell remains stable. This is thought to reflect the activity of a contrast gain control ("normalization") that has very broad tuning on the relevant stimulus dimension. Contrast invariant tuning on a particular dimension is probably necessary for reliable representation of stimuli on that dimension. In the lateral geniculate nucleus (LGN), V1, and V2 of anesthetized macaque, we measured chromatic tuning of neurons at several contrasts to characterize the gain controls and identify cells that might be important for representing color. We estimated separately the chromatic signature of the linear receptive field and that of the gain control. In the LGN, we found normalization in magnocellular cells and cells receiving excitatory S-cone input but not in parvocellular cells or those receiving inhibitory S-cone input. We found normalization in all types of cortical neurons. Among cells that preferred achromatic modulation, or modulation along intermediate directions in color space (making them responsive to both achromatic and chromatic stimuli), normalization was driven by mechanisms tuned to a restricted range of directions in color space, close to achromatic. As a result, chromatic tuning varied with contrast. Among the relatively few cells that strongly preferred chromatic modulation, normalization was driven by mechanisms sensitive to modulation along all directions in color space, especially isoluminant. As a result, chromatic tuning changed little with contrast. To the extent that contrast invariant tuning is important in representing chromaticity, relatively few cortical neurons are involved.

Short-term depression in thalamocortical synapses of cat primary visual cortex

Neurons in primary visual cortex exhibit several nonlinearities in their responses to visual stimuli, including response decrements to repeated stimuli, contrast-dependent phase advance, contrast saturation, and cross-orientation suppression. Thalamocortical synaptic depression has been implicated in these phenomena but has not been examined directly in visual cortex in vivo. We assessed depression of visual thalamocortical synapses in vivo using 20-100 Hz trains of electrical stimuli delivered to the LGN. Cortical cells receiving direct input from the LGN, identified by short latency and low jitter of LGN-evoked PSPs, showed moderate reductions in PSP amplitude during the fastest trains. Cells receiving indirect input from the thalamus via other cortical excitatory neurons show a marked reduction in PSP amplitude during a train, which could be explained either by synaptic depression in corticocortical synapses or by an inhibition-mediated suppression of the firing of their afferents. Reducing spontaneous activity in the LGN (by retinal blockade) unmasked additional depression at the thalamocortical synapse but only for the first stimulus in the train. That is, the first PSP was increased in amplitude relative to the unblocked condition, but subsequent responses were essentially unchanged. Thus, the synapses are maintained at significant levels of depression by spontaneous activity. These findings constrain the role that thalamocortical depression can play in shaping cortical responses to visual stimuli.

Adaptive temporal integration of motion in direction-selective neurons in macaque visual cortex

Direction-selective neurons in the primary visual cortex (V1) and the extrastriate motion area MT/V5 constitute a critical channel that links early cortical mechanisms of spatiotemporal integration to downstream signals that underlie motion perception. We studied how temporal integration in direction-selective cells depends on speed, spatial frequency (SF), and contrast using randomly moving sinusoidal gratings and spike-triggered average (STA) analysis. The window of temporal integration revealed by the STAs varied substantially with stimulus parameters, extending farther back in time for slow motion, high SF, and low contrast. At low speeds and high SF, STA peaks were larger, indicating that a single spike often conveyed more information about the stimulus under conditions in which the mean firing rate was very low. The observed trends were similar in V1 and MT and offer a physiological correlate for a large body of psychophysical data on temporal integration. We applied the same visual stimuli to a model of motion detection based on oriented linear filters (a motion energy model) that incorporated an integrate-and-fire mechanism and found that it did not account for the neuronal data. Our results show that cortical motion processing in V1 and in MT is highly nonlinear and stimulus dependent. They cast considerable doubt on the ability of simple oriented filter models to account for the output of direction-selective neurons in a general manner. Finally, they suggest that spike rate tuning functions may miss important aspects of the neural coding of motion for stimulus conditions that evoke low firing rates.

A nonlinear model of the behavior of simple cells in visual cortex

Despite their structured receptive fields (RFs) and the strong linear components in their responses, most simple cells in mammalian visual cortex exhibit nonlinear behaviors. Besides the contrast-response function, nonlinearities are evident in various types of failure at superposition tasks, in the disagreement between direction indices computed from drifting and counterphase flickering gratings, in various forms of response suppression (including end- and side-stopping, spatial-frequency-specific inhibition and cross-orientation inhibition), in the advance of phase with increasing contrast, and in phase-insensitive and frequency-doubled responses to counterphase flickering gratings. These behaviors suggest that nonlinearities are involved in the operation of simple cells, but current models fail to explain them. A quantitative model is presented here that purports to describe basic and common principles of operation for all visual cortical cells. Simple cells are described as receiving afferents from multiple subunits that differ in their individual RFs and temporal impulse responses (TIRs). Subunits are independent and perform a spatial integration across their RFs followed by halfwave rectification and temporal convolution with their TIRs. This parallel operation yields a set of temporal functions representing each subunit's contribution to the membrane potential of the host cell, whose final form is given by the weighted sum of all subunits' contributions. By varying the number of subunits and their particular characteristics, different instances of the model are obtained each of which displays a different set of behaviors. Extensive simulation results are presented that illustrate how all of the reported nonlinear behaviors of simple cells arise from these multi-subunit organizations.

Comparison of different models of orientation selectivity based on distinct intracortical inhibition rules

Aim of this work is to present simple models of orientation selectivity in the visual cortex, which do not require massive computational effort. Three different models are compared, in order to gain deeper insight into the structure of cortical circuits generating inhibitory signals. All models represent a single hypercolumn. They differ as to the arrangement of inhibitory connections: in the first ("antiphase inhibition model") inhibition is in phase opposition with excitation, but with a similar orientation tuning; in the second ("in-phase inhibition model"), inhibition is in phase with excitation, but with larger orientation tuning. In these two models the orientation width of inhibition increases with contrast. Finally, a third model ("center-surround model") assumes that inhibition comes from the same cells providing excitation, hence the inhibition tuning is contrast-independent. All models, with suitable values of the intracortical synapse parameters, are able to mimic experimental results in the literature. A few differences are evident between the "center-surround model" and the other two, especially as to the dependence of cortical cell response on spatial frequency. The models can represent practical tools to test hypotheses on the disposition of cortical synapses avoiding massive computational efforts.

Visual cortex neurons of monkeys and cats: temporal dynamics of the spatial frequency response function

We measured the responses of striate cortex neurons as a function of spatial frequency on a fine time scale, over the course of an interval that is comparable to the duration of a single fixation (200 ms). Stationary gratings were flashed on for 200 ms and then off for 300 ms; the responses were analyzed at sequential 1-ms intervals. We found that 1) the preferred spatial frequency shifts through time from low frequencies to high frequencies, 2) the latency of the response increases as a function of spatial frequency, and 3) the poststimulus time histograms (PSTHs) are relatively shape-invariant across spatial frequency. The dynamic shifts in preferred spatial frequency appear to be a simple consequence of the latency shifts and the transient nature of the PSTH. The effects of these dynamic shifts on the coding of spatial frequency information are examined within the context of several different temporal integration strategies, and pattern-detection performance is determined as a function of the interval of integration, following response onset. The findings are considered within the context of related investigations as well as a number of functional issues: motion selectivity in depth, "coarse-to-fine" processing, direction selectivity, latency as a code for stimulus attributes, and behavioral response latency. Finally, we demonstrate that the results are qualitatively consistent with a simple feedforward model, similar to the one originally proposed in 1962 by Hubel and Wiesel, that incorporates measured differences in the response latencies and the receptive field sizes of different lateral geniculate nucleus inputs.

Influence of contrast on orientation and temporal frequency tuning in ferret primary visual cortex

Neurons in primary visual cortex are highly sensitive to the contrast, orientation, and temporal frequency of a visual stimulus. These three stimulus properties can be varied independently of one another, raising the question of how they interact to influence neuronal responses. We recorded from individual neurons in ferret primary visual cortex to determine the influence of stimulus contrast on orientation tuning, temporal-frequency tuning, and latency to visual response. Results show that orientation-tuning bandwidth is not affected by contrast level. Thus neurons in ferret visual cortex display contrast-invariant orientation tuning. Stimulus contrast does, however, influence the structure of orientation-tuning curves as measures of circular variance vary inversely with contrast for both simple and complex cells. This change in circular variance depends, in part, on a contrast-dependent change in the ratio of null to preferred orientation responses. Stimulus contrast also has an influence on the temporal-frequency tuning of cortical neurons. Both simple and complex cells display a contrast-dependent rightward shift in their temporal frequency-tuning curves that results in an increase in the highest temporal frequency needed to produce a half-maximum response (TF(50)). Results show that the degree of the contrast-dependent increase in TF(50) is similar for cortical neurons and neurons in the lateral geniculate nucleus (LGN) and indicate that subcortical mechanisms likely play a major role in establishing the degree of effect displayed by downstream neurons. Finally, results show that LGN and cortical neurons experience a contrast-dependent phase advance in their visual response. This phase advance is most pronounced for cortical neurons indicating a role for both subcortical and cortical mechanisms.

Different roles for simple-cell and complex-cell inhibition in V1

Previously, we proposed a model of the circuitry underlying simple-cell responses in cat primary visual cortex (V1) layer 4. We argued that the ordered arrangement of lateral geniculate nucleus inputs to a simple cell must be supplemented by a component of feedforward inhibition that is untuned for orientation and responds to high temporal frequencies to explain the sharp contrast-invariant orientation tuning and low-pass temporal frequency tuning of simple cells. The temporal tuning also requires a significant NMDA component in geniculocortical synapses. Recent experiments have revealed cat V1 layer 4 inhibitory neurons with two distinct types of receptive fields (RFs): complex RFs with mixed ON/OFF responses lacking in orientation tuning, and simple RFs with normal, sharp-orientation tuning (although, some respond to all orientations). We show that complex inhibitory neurons can provide the inhibition needed to explain simple-cell response properties. Given this complex cell inhibition, antiphase or "push-pull" inhibition from tuned simple inhibitory neurons acts to sharpen spatial frequency tuning, lower responses to low temporal frequency stimuli, and increase the stability of cortical activity.

Learning and adaptation in a recurrent model of V1 orientation selectivity

Learning and adaptation in the domain of orientation processing are among the most studied topics in the literature. However, little effort has been devoted to explaining the diverse array of experimental findings via a physiologically based model. We have started to address this issue in the framework of the recurrent model of V1 orientation selectivity and found that reported changes in V1 orientation tuning curves after learning and adaptation can both be explained with the model. Specifically, the sharpening of orientation tuning curves near the trained orientation after learning can be accounted for by slightly reducing net excitatory connections to cells around the trained orientation, while the broadening and peak shift of the tuning curves after adaptation can be reproduced by appropriately scaling down both excitation and inhibition around the adapted orientation. In addition, we investigated the perceptual consequences of the tuning curve changes induced by learning and adaptation using signal detection theory. We found that in the case of learning, the physiological changes can account for the psychophysical data well. In the case of adaptation, however, there is a clear discrepancy between the psychophysical data from alert human subjects and the physiological data from anesthetized animals. Instead, human adaptation studies can be better accounted for by the learning data from behaving animals. Our work suggests that adaptation in behaving subjects may be viewed as a short-term form of learning.

Interacting roles of attention and visual salience in V4

Attention increases the contrast gain of V4 neurons, causing them to respond to an attended stimulus as though its contrast had increased. When multiple stimuli appear within a neuron's receptive field (RF), the neuron responds primarily to the attended stimulus. This suggests that cortical cells may be "hard wired" to respond preferentially to the highest-contrast stimulus in their RF, and neural systems for attention capitalize on this mechanism by dynamically increasing the effective contrast of the stimulus that is task relevant. To test this, we varied the relative contrast of two stimuli within the recorded neurons' RFs, while the monkeys attended away to another location. Increasing the physical contrast of one stimulus caused V4 neurons to respond preferentially to that stimulus and reduced their responses to competing stimuli. When attention was directed to the lower-contrast stimulus, it partially overcame the influence of a competing, higher-contrast stimulus.

A synaptic explanation of suppression in visual cortex

The responses of neurons in the primary visual cortex (V1) are suppressed by mask stimuli that do not elicit responses if presented alone. This suppression is widely believed to be mediated by intracortical inhibition. As an alternative, we propose that it can be explained by thalamocortical synaptic depression. This explanation correctly predicts that suppression is monocular, immune to cortical adaptation, and occurs for mask stimuli that elicit responses in the thalamus but not in the cortex. Depression also explains other phenomena previously ascribed to intracortical inhibition. It explains why responses saturate at high stimulus contrast, whereas selectivity for orientation and spatial frequency is invariant with contrast. It explains why transient responses to flashed stimuli are nonlinear, whereas spatial summation is primarily linear. These results suggest that the very first synapses into the cortex, and not the cortical network, may account for important response properties of V1 neurons.

Synaptic depression increases the selectivity of a neuron to its preferred pattern and binarizes the neural code

The preferred pattern of a neuron is defined here by the set of features detected by its excitatory inputs. It is shown that the Leaky integrate-and-fire (LIF) model of a neuron has a poor selectivity to its preferred pattern. Its response is determined by the total current injected by input spike trains. Thus, a few inputs with a high activity (an incomplete pattern) can elicit the same response as many inputs (a complete pattern) with a weak activity. A theoretical model of depressing synapse with linear recovery is proposed which eliminates this problem. Using this model, the time-averaged current injected in the soma by a spike train becomes independent on its frequency. The neural code thus becomes binary, and the response strength of the target neuron depends only on the number of active inputs. Simulations show that a biological model of strong synaptic depression has effects similar to those of the ideal linear model. The best selectivity is obtained with long somatic decay time constants (>50 ms) and with depression recovery time constants larger or equal to the somatic decay time constant. Thus, by eliminating information carried in the input firing rate, a neuron can improve its pattern recognition performance.

Suppression without inhibition in visual cortex

Neurons in primary visual cortex (V1) are thought to receive inhibition from other V1 neurons selective for a variety of orientations. Evidence for this inhibition is commonly found in cross-orientation suppression: responses of a V1 neuron to optimally oriented bars are suppressed by superimposed mask bars of different orientation. We show, however, that suppression is unlikely to result from intracortical inhibition. First, suppression can be obtained with masks drifting too rapidly to elicit much of a response in cortex. Second, suppression is immune to hyperpolarization (through visual adaptation) of cortical neurons responding to the mask. Signals mediating suppression might originate in thalamus, rather than in cortex. Thalamic neurons exhibit some suppression; additional suppression might arise from depression at thalamocortical synapses. The mechanisms of suppression are subcortical and possibly include the very first synapse into cortex.

Visual cortex neurons of monkeys and cats: temporal dynamics of the contrast response function

Cortical neurons display two fundamental nonlinear response characteristics: contrast-set gain control (also termed contrast normalization) and response expansion (also termed half-squaring). These nonlinearities could play an important role in forming and maintaining stimulus selectivity during natural viewing, but only if they operate well within the time frame of a single fixation. To analyze the temporal dynamics of these nonlinearities, we measured the responses of individual neurons, recorded from the primary visual cortex of monkeys and cats, as a function of the contrast of transient stationary gratings that were presented for a brief interval (200 ms). We then examined 1) the temporal response profile (i.e., the post stimulus time histogram) as a function of contrast and 2) the contrast response function throughout the course of the temporal response. We found that the shape and complexity of the temporal response profile varies considerably from cell to cell. However, within a given cell, the shape remains relatively invariant as a function of contrast and appears to be simply scaled and shifted. Stated quantitatively, approximately 95% of the variation in the temporal responses as a function of contrast could be accounted for by scaling and shifting the average poststimulus time histogram. Equivalently, we found that the overall shape of the contrast response function (measured every 2 ms) remains relatively invariant from the onset through the entire temporal response. Further, the contrast-set gain control and the response expansion are fully expressed within the first 10 ms after the onset of the response. Stated quantitatively, the same, scaled Naka-Rushton equation (with the same half-saturation contrast and expansive response exponent) provides a good fit to the contrast response function from the first 10 ms through the last 10 ms of the temporal response. Based upon these measurements, it appears as though the two nonlinear properties, contrast-set gain control and response expansion, are present in full strength, virtually instantaneously, at the onset of the response. This observation suggests that response expansion and contrast-set gain control can influence the performance of visual cortex neurons very early in a single fixation, based on the contrast within that fixation. In the DISCUSSION, we consider the implications of the results within the context of 1) slower types of contrast gain control, 2) discrimination performance, 3) drifting steady-state measurements, 4) functional models that incorporate response expansion and contrast normalization, and 5) structural models of the biochemical and biophysical neural mechanisms.

Constraints on the source of short-term motion adaptation in macaque area MT. I. the role of input and intrinsic mechanisms

Neurons in area MT, a motion-sensitive area of extrastriate cortex, respond to a step of target velocity with a transient-sustained firing pattern. The transition from a high initial firing rate to a lower sustained rate occurs over a time course of 20-80 ms and is considered a form of short-term adaptation. The present paper asks whether adaptation is due to input-specific mechanisms such as short-term synaptic depression or if it results from intrinsic cellular mechanisms such as spike-rate adaptation. We assessed the contribution of input-specific mechanisms by using a condition/test paradigm to measure the spatial scale of adaptation. Conditioning and test stimuli were placed within MT receptive fields but were spatially segregated so that the two stimuli would activate different populations of inputs from the primary visual cortex (V1). Conditioning motion at one visual location caused a reduction of the transient firing to subsequent test motion at a second location. The adaptation field, estimated as the region of visual space where conditioning motion caused adaptation, was always larger than the MT receptive field. Use of the same stimulus configuration while recording from direction-selective neurons in V1 failed to demonstrate either adaptation or the transient-sustained response pattern that is the signature of short-term adaptation in MT. We conclude that the shift from transient to sustained firing in MT cells does not result from an input-specific mechanism applied to inputs from V1 because it operates over a wider range of the visual field than is covered by receptive fields of V1 neurons. We used a direct analysis of MT neuron spike trains for many repetitions of the same motion stimulus to assess the contribution to adaptation of intrinsic cellular mechanisms related to spiking. On a trial-by-trial basis, there was no correlation between number of spikes in the transient interval and the interval immediately after the transient period. This is opposite the prediction that there should be a correlation if spikes cause adaptation directly. Further, the transient was suppressed or extinguished, not delayed, in trials in which the neuron emitted zero spikes during the interval that showed a transient in average firing rate. We conclude that the transition from transient to sustained firing in neurons in area MT is caused by mechanisms that are neither input-specific nor controlled by the spiking of the adapting neuron. We propose that the short-term adaptation observed in area MT emerges from the intracortical circuit within MT.

Constraints on the source of short-term motion adaptation in macaque area MT. II. tuning of neural circuit mechanisms

Neurons in area MT, a motion-sensitive area of extrastriate cortex, respond to a step of target velocity with a transient-sustained firing pattern. The transition from a high initial firing rate to a lower sustained rate occurs over a time course of 20-80 ms and is considered a form of short-term adaptation. In the present paper, we compared the tuning of the adaptation to the neuron's tuning to direction and speed. The tuning of adaptation was measured with a condition/test paradigm in which a testing motion of the preferred direction and speed of the neuron under study was preceded by a conditioning motion: the direction and speed of the conditioning motion were varied systematically. The response to the test motion depended strongly on the direction of the conditioning motion. It was suppressed in almost all neurons by conditioning motion in the same direction and could be either suppressed or enhanced by conditioning motion in the opposite direction. Even in neurons that showed suppression for target motion in the nonpreferred direction, the adaptation and response direction tuning were the same. The speed tuning of adaptation was linked much less tightly to the speed tuning of the response of the neuron under study. For just more than 50% of neurons, the preferred speed of adaptation was more than 1 log unit different from the preferred response speed. Many neurons responded best when slow motions were followed by faster motions (acceleration) or vice versa (deceleration), suggesting that MT neurons may encode information about the change of target velocity over time. Finally, adaptation by conditioning motions of different directions, but not different speeds, altered the latency of the response to the test motion. The adaptation of latency recovered with shorter intervals between the conditioning and test motions than did the adaptation of response size, suggesting that latency and amplitude adaptation are mediated by separate mechanisms. Taken together with the companion paper, our data suggest that short-term motion adaptation in MT is a consequence of the neural circuit in MT and is not mediated by either input-specific mechanisms or intrinsic mechanisms related to the spiking of individual neurons. The circuit responsible for adaptation is tuned for both speed and direction and has the same direction tuning as the circuit responsible for the initial response of MT neurons.

Opponent inhibition: a developmental model of layer 4 of the neocortical circuit

We model the development of the functional circuit of layer 4 (the input-recipient layer) of cat primary visual cortex. The observed thalamocortical and intracortical circuitry codevelop under Hebb-like synaptic plasticity. Hebbian development yields opponent inhibition: inhibition evoked by stimuli anticorrelated with those that excite a cell. Strong opponent inhibition enables recognition of stimulus orientation in a manner invariant to stimulus contrast. These principles may apply to cortex more generally: Hebb-like plasticity can guide layer 4 of any piece of cortex to create opposition between anticorrelated stimulus pairs, and this enables recognition of specific stimulus patterns in a manner invariant to stimulus magnitude. Properties that are invariant across a cortical column are predicted to be those shared by opponent stimulus pairs; this contrasts with the common idea that a column represents cells with similar response properties.

Local correlation-based circuitry can account for responses to multi-grating stimuli in a model of cat V1

In cortical simple cells of cat striate cortex, the response to a visual stimulus of the preferred orientation is partially suppressed by simultaneous presentation of a stimulus at the orthogonal orientation, an effect known as "cross-orientation inhibition." It has been argued that this is due to the presence of inhibitory connections between cells tuned for different orientations, but intracellular studies suggest that simple cells receive inhibitory input primarily from cells with similar orientation tuning. Furthermore, response suppression can be elicited by a variety of nonpreferred stimuli at all orientations. Here we study a model circuit that was presented previously to address many aspects of simple cell orientation tuning, which is based on local intracortical connectivity between cells of similar orientation tuning. We show that this model circuit can account for many aspects of cross-orientation inhibition and, more generally, of response suppression by nonpreferred stimuli and of other nonlinear properties of responses to stimulation with multiple gratings.